Title

Author

Date of Award

3-16-2015

Document Type

Thesis and Dissertation

Degree Name

Master of Science (MS)

Department

School of Information Technology: Information Systems

First Advisor

Elahe Javadi

Abstract

This thesis applies multi-way sensitivity analysis for the winning algorithm in the Knowledge Discovery in Data Mining (KDD) cup competition 2014 -`Predicting Excitement at Donors.org'. Because of the highly advanced nature of this competition, analyzing the winning solution under a variety of different conditions provides insight about each of the models the winning team has used in the competition. The study follows Cross Industry Standard Process (CRISP) for data mining to study the steps taken to prepare, model and evaluate the model. The thesis focuses on a gradient boosting model. After careful examination of the models created by the researchers who won the cup, this thesis performed multi-way sensitivity analysis on the model named above. The sensitivity analysis performed in this study focuses on key parameters in each of those algorithms and examines the influence of those parameters on the accuracy of the predictions.